aio-geo

Audit URL

audit_url

Read-onlyIdempotent

Run a structural AI Search Readiness audit for a public URL or domain. Returns pillar scores, recommendations, a brief link, and a CLI command. Not an LLM ranking. No API key required.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`url`	No	Public domain or https URL to inspect (for example example.com or https://example.com). Localhost and private IPs are blocked.
`domain`	No	Alias of url. Use either url or domain, not both.

Output Schema

TableJSON Schema

Name	Required	Description
`ok`	Yes	True when the audit completed
`cli`	No	Matching CLI command
`domain`	Yes	Normalized hostname
`pillars`	No	Per-pillar structural scores
`refusal`	No	Ranking-refusal copy
`briefUrl`	No	Shareable public brief URL
`persisted`	No	True if the scan was saved
`confidence`	No	0-1 crawl confidence
`disclaimer`	No
`isSynthetic`	No	True only if the crawl failed closed
`methodology`	No	Scoring methodology id
`overallScore`	Yes	Structural readiness score 0-100. Not an LLM ranking.
`recommendations`	No	Ranked structural gaps to fix

TDQS

A4.5/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds valuable context by disclosing 'No API key required,' the nature of the audit ('structural'), and the output composition (pillar scores, recommendations, brief link, CLI command). It also clarifies it is not an LLM ranking, preventing misuse.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two concise sentences, front-loading the main function and outputs, then adding exclusions. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a modest schema and full annotations; the description covers purpose, output, authentication, and exclusions. The existence of an output schema means return details need not be described, but the description still lists key outputs, making it complete for the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with clear descriptions of url and domain. The description does not add substantive parameter-level meaning beyond repeating the public URL/domain scope, so it relies on the schema, earning the baseline score of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's purpose with the verb 'Run' and resource 'structural AI Search Readiness audit for a public URL or domain.' It also lists the specific outputs (pillar scores, recommendations, brief link, CLI command), which distinguishes it from siblings like rescore_url by stating 'Not an LLM ranking.'

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides context that the tool is for structural audits and explicitly states 'Not an LLM ranking,' which helps differentiate it from scoring tools. However, it does not name alternative sibling tools or give explicit when-to-use vs when-not-to-use guidance beyond this exclusion.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0

Disambiguation2/5

The tool set has significant overlap: audit_url and rescore_url both run the same structural audit, and list_fixes vs apply_fix both generate fix packs (with apply_fix differing only by optional write access). This blurs boundaries and forces agents to rely on subtle wording to choose correctly.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (apply_fix, audit_url, check_health, get_brief, list_fixes, rescore_url). The verbs are distinct and clearly indicate the action, making the naming predictable and easy to parse.

Tool Count5/5

With 6 tools, the server is well-scoped for its purpose. Each tool covers a specific step in the audit-to-fix workflow without excessive bloat or noticeable omissions.

Completeness4/5

The core workflow is covered: audit (audit_url/rescore_url), brief (get_brief), and fixes (list_fixes/apply_fix), plus health check. Minor gap: no explicit tool to view detailed individual recommendations or manage applied fixes, but agents can work around this using the available outputs.

Resources

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